Nadia.Rochdi @nrcan.gc Richard Fernandes Sylvain Leblanc Chris Butson Abdel Abuelguesim

Mapping LAI over CanadaMethods and Results Nadia.Rochdi @nrcan.gc.ca Richard Fernandes Sylvain Leblanc Chris Butson Abdel Abuelguesim Peter White Shusen Wang

Why and How mapping LAI Objective Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models Main Steps • Introduction • Field LAI measurements through optical techniques. • Basic satellite LAI retrieval algorithm : • Which regression method should be used ? • Does vegetation indice make difference? • Does LAI algorithm show temporal consistency? • Inter-comparison of global product : MODIS-POLDER-VEGETATION

Mapping LAI Objective Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models Main Steps • Introduction • Field LAI measurements through optical techniques. • Basic satellite LAI retrieval algorithm : • Which regression method should be used ? • Does vegetation indice make difference? • Does LAI algorithm show temporal consistency? • Inter-comparison of global product : MODIS-POLDER-VEGETATION

1. Introduction Definition – just to remind us! • Projected “green” foliage area per unit of ground surface area(m2/m2): • Important biophysical property of vegetation canopies used in ecological modeling (used in Myneni et al. 2002 ; Running et al., 1989). • Referred as Green Leaf Area Index (GLAI). • LAI is defined as half the total foliage surface of all sides per unit of surface projected on the local horizontal datum(used in Chen and Black, 1992; Fernandes et al., 2001; Liu et al., 2002; Chen et al., 2003). Well adapted for flat leaves : grass, crops, deciduous forest In conifers shoot is considered as the foliage element: Needles organization should be taken into account

1. Introduction Pinus Bancksiana (Jack Pine) Picea-Mariana (Black Spruce) Pinus-sylvestris (Scot Pine) • effect on scattering within canopies: • Modify the shoot scattering albedo • (Smolander et al. 2003) • Modify the canopy BRDF • (5SCALE simulation on Old black spruce) STAR- a possible show stopper • Shoot silhouette to total needle area ratio ‘STAR’ (Oker-Blom & al 1988) • STAR variability: [0.09 0.22]

2. Field LAI Measurements using Optical Techniques • Canopy gap fraction • Plant area index PAI: Woody Area Index Needle-to-shoot area ratio • Projection of unit foliage in  direction G(): Same for foliage and woody materials G() always close to 0.5 for  =57.5° for any random foliage azimuth angle distribution(Warren Wilson and Reeve 1959, Weiss et al 2004) Projected area of the leaves G() Average Leaf Inclination Factor In-situ optical PAI/LAI estimation • Foliage elements clumping ():Same for foliage and woody materials

2. Field LAI Measurements using Optical Techniques • Method2:Rely on Cauchy’s theorem (Miller 1967): PAI ( 55-60) • Both methods should match if they sample the same stand. PAI-Miller ( 10-80) Methods • Method1: Invert gap fraction measurements near 57º (G()=0.5)

2. Field LAI Measurements using Optical Techniques • Gap size distribution (Chen et al 1995, corrected in Leblanc 2002) Lp(): Projected LAI : Gap size Wp(): Foliage element mean projection on the ground Fm(0,): Measured accumulated gap fraction larger than 0 Fmr(0,): Gap fraction for the canopy without large gaps =10° Digital Number Accumulated Gap Fraction Azimuth Angle Gap Size (pixel) But what about clumping? • This method throws away big gaps (discontinuous media). • Limited when clumping could impact small gaps (should be test in agriculture)

2. Field LAI Measurements using Optical Techniques Digital hemispherical Photos (DHP) Software • Provide clumping as function of view angle • Deals with sub-pixel gaps

2. Field LAI Measurements using Optical Techniques • Good correlation between both techniques but with a systematic bias. Scattering effect on DHP & TRAC inability to see very small gaps • Improvement occurs when removing sub-pixel gap in DHP processing. • Possibility to get () angular variation from DHP since TRAC measurements is solar zenith dependent R2=0.95 R2=0.9 Gap Fraction DHP Gap Fraction DHP Gap Fraction TRAC Gap Fraction TRAC Can we replace the TRAC with DHP ?

3. Satellite LAI Retrieval Algorithm LAI LAI VALERI data on conifers (Larose forest) and SR landsat ETM+ SR Can we make a map using in-situ data and co-incident imagery • Large errors in both LAI and VI are common. • Classical linear regression will be biased. • Thiel-Sen Method is unbiased, requires no error information and is robust to 29% outliers (Kendall and Stewart Advanced Theory Statistics)

3. Satellite LAI Retrieval Algorithm • Errors on Lai retrieval were assessed in regards to fouratmospheric correction approaches: • Aerosol optical depth constant for all scenes (AOD550=0.06) • no-atmospheric correction (AOD550=0) • scene Dense Dark Vegetation (DDV) (scene-dependent AOD550) • MODIS AOD550 measurements. Can we make consistent maps of the same area from “Landsat ETM+”? • Overlap reflectance was to some extent consistent but depends on spectral band : visible band show big differences (32-73%). • LAI consistency errors do not depend on atmospheric correction method: 0.6 unit LAI (25%) Color composite from SPOT4-VEGETATION 20 Landsat ETM+ images from 7-25 days apart

3. Satellite LAI Retrieval Algorithm LAI R2=0.86 ISR SR vs RSR vs ISR • Vegetation indices (VI): • Simple ratio • Reduced Simple Ratio • Infrared Simple Ratio • Regressions between ISR and LAI are as satisfactory or better than RSR & SR Conifer forests

3. Satellite LAI Retrieval Algorithm SWIR Red Atmospheric Noise should be considered when using VI’s • Errors in aerosol optical depth (around a baseline value of 0.10) are respectively equal to +0.10 (solid lines) and –0.10 (broken lines) • RSR relative errors around  20% for typical uncertainties in aerosol optical depth • ISR seems to be more robust estimator than RSR showing less sensitivity to atmospheric conditions

3. Satellite LAI Retrieval Algorithm Impact on Time Series of LAI • ISR shows consistent temporal behavior in relation to the SR one. • High LAI estimations form ISR in the end of growing season. 10 days maximum SPOT-VEGETATION “Sparse shrub and grass”

Needleaf Forest MODIS LAIx20 POLDER LAIx20 Needleaf Forest Broadleaf Forest Crops Pastures/Grasses Tundra/Barren -4 -2 0 2 4 4. Inter-Comparison of Global Products LAI Difference VGT LAI * 20 VGT LAI * 20 LAND USE OF COMPARED REGIONS MODIS LAI – VGT LAI (JUNE 2000) Crops Needleaf Forest Broadleaf Forest Crops Pastures/Grasses Tundra/Barren -4 -2 0 2 4 LAI Difference MODIS LAIx20 POLDER LAIx20 VGT LAI * 20 VGT LAI * 20 LAND USE OF COMPARED REGIONS POLDER LAI June1997 – VGT LAI June 1998 Large Area Intercomparison • Considering 1 year difference in the data and 9km scale POLDER and VGT are relatively consistent • MODIS LAI estimations are higher than VGT and POLDER over Forests and tundra • Over crops and pasture MODIS seems to show unreasonable LAI values range

Main conclusions • Stick to one LAI definition even if it is arbitrary (invariance). • Needle to shoot area ratio is problematic. • Digital hemispherical photography can provide consistent in-situ measurements of “PAI”. • Atmospheric contamination (especially sub-pixel clouds) can cause big problems in “products”.

What we need • Temporal validation using simple consistent methods (e.g. plantwatch method of quarters, using simple cheap cameras placed in-situ with “cell phone” links, etc.) • We need more work to determine if we can actually see within shoots using passive optical imagery. • More attention is needed with respect to the relationship between clumping and factors like angle, scale, species, crown shape... • We should consider either a global LAI effort (a la global land cover) or at least a global LAI validation effort under the auspices of GEO (global earth observations).

Nadia.Rochdi @nrcan.gc Richard Fernandes Sylvain Leblanc Chris Butson Abdel Abuelguesim